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# %pip install geoai-py
# %pip install geoai-py
Import libraries¶
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import geoai
import geoai
Download sample data¶
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raster_url = (
"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train.tif"
)
vector_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train_buildings.geojson"
raster_url = (
"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train.tif"
)
vector_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train_buildings.geojson"
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raster_path = geoai.download_file(raster_url)
raster_path = geoai.download_file(raster_url)
naip_train.tif: 0%| | 0.00/12.1M [00:00<?, ?B/s]
naip_train.tif: 80%|████████ | 9.71M/12.1M [00:00<00:00, 102MB/s]
naip_train.tif: 100%|██████████| 12.1M/12.1M [00:00<00:00, 103MB/s]
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vector_path = geoai.download_file(vector_url)
vector_path = geoai.download_file(vector_url)
naip_train_buildings.geojson: 0%| | 0.00/456k [00:00<?, ?B/s]
naip_train_buildings.geojson: 100%|██████████| 456k/456k [00:00<00:00, 28.7MB/s]
Initialize building footprint extraction pretrained model¶
The pretained model is adapted from the Esri building footprint extraction model for the USA. Credits to Esri for the model.
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extractor = geoai.BuildingFootprintExtractor()
extractor = geoai.BuildingFootprintExtractor()
Model path not specified, downloading from Hugging Face...
Model downloaded to: /home/runner/.cache/huggingface/hub/models--giswqs--geoai/snapshots/92a3d4371b88466e0fc1ab3b0964f45782fca4d0/building_footprints_usa.pth Model loaded successfully
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mask_path = extractor.save_masks_as_geotiff(
raster_path=raster_path,
output_path="building_masks.tif",
confidence_threshold=0.5,
mask_threshold=0.5,
)
mask_path = extractor.save_masks_as_geotiff(
raster_path=raster_path,
output_path="building_masks.tif",
confidence_threshold=0.5,
mask_threshold=0.5,
)
Processing masks with parameters: - Confidence threshold: 0.5 - Chip size: (512, 512) - Mask threshold: 0.5 Dataset initialized with 3 rows and 5 columns of chips Image dimensions: 2503 x 1126 pixels Chip size: 512 x 512 pixels CRS: EPSG:26911
Processing raster with 4 batches
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Resizing masks at image edges (set verbose=True for details)
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Object masks saved to building_masks.tif
Convert raster to vector
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gdf = extractor.masks_to_vector(
mask_path=mask_path,
output_path="building_masks.geojson",
simplify_tolerance=1.0,
)
gdf = extractor.masks_to_vector(
mask_path=mask_path,
output_path="building_masks.geojson",
simplify_tolerance=1.0,
)
Converting mask to GeoJSON with parameters: - Mask threshold: 0.5 - Min object area: 100 - Max object area: None - Simplify tolerance: 1.0 - NMS IoU threshold: 0.5 - Regularize objects: True - Angle threshold: 15° from 90° - Rectangularity threshold: 70.0% Mask dimensions: (1126, 2503) Mask value range: 0 to 255 Found 642 potential objects
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78%|███████▊ | 503/642 [00:00<00:00, 692.96it/s]
89%|████████▉ | 573/642 [00:00<00:00, 692.38it/s]
100%|██████████| 642/642 [00:00<00:00, 700.45it/s]
Created 623 valid polygons
Object count after NMS filtering: 623 Regularizing 623 objects... - Angle threshold: 15° from 90° - Min orthogonality: 30.0% of angles - Min rectangularity: 70.0% of bounding box area
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43%|████▎ | 265/623 [00:00<00:00, 1323.99it/s]
65%|██████▍ | 404/623 [00:00<00:00, 1352.26it/s]
89%|████████▉ | 556/623 [00:00<00:00, 1416.45it/s]
100%|██████████| 623/623 [00:00<00:00, 1401.12it/s]
Regularization completed: - Total objects: 623 - Rectangular objects: 619 (99.4%) - Other regularized objects: 0 (0.0%) - Unmodified objects: 4 (0.6%) Saved 623 objects to building_masks.geojson
Option 2: Extract building footprints as vector¶
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output_path = "naip_buildings.geojson"
gdf = extractor.process_raster(
raster_path,
output_path="buildings.geojson",
batch_size=4,
confidence_threshold=0.5,
overlap=0.25,
nms_iou_threshold=0.5,
min_object_area=100,
max_object_area=None,
mask_threshold=0.5,
simplify_tolerance=1.0,
)
output_path = "naip_buildings.geojson"
gdf = extractor.process_raster(
raster_path,
output_path="buildings.geojson",
batch_size=4,
confidence_threshold=0.5,
overlap=0.25,
nms_iou_threshold=0.5,
min_object_area=100,
max_object_area=None,
mask_threshold=0.5,
simplify_tolerance=1.0,
)
Processing with parameters: - Confidence threshold: 0.5 - Tile overlap: 0.25 - Chip size: (512, 512) - NMS IoU threshold: 0.5 - Mask threshold: 0.5 - Min object area: 100 - Max object area: None - Simplify tolerance: 1.0 - Filter edge objects: True - Edge buffer size: 20 pixels Dataset initialized with 3 rows and 5 columns of chips Image dimensions: 2503 x 1126 pixels Chip size: 512 x 512 pixels CRS: EPSG:26911
Processing raster with 4 batches
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100%|██████████| 4/4 [00:41<00:00, 9.94s/it]
100%|██████████| 4/4 [00:41<00:00, 10.35s/it]
Objects before filtering: 684 Objects after filtering: 679 Saved 679 objects to buildings.geojson
Regularize building footprints¶
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gdf_regularized = extractor.regularize_buildings(
gdf=gdf,
min_area=100,
angle_threshold=15,
orthogonality_threshold=0.3,
rectangularity_threshold=0.7,
)
gdf_regularized = extractor.regularize_buildings(
gdf=gdf,
min_area=100,
angle_threshold=15,
orthogonality_threshold=0.3,
rectangularity_threshold=0.7,
)
Regularizing 679 objects... - Angle threshold: 15° from 90° - Min orthogonality: 30.0% of angles - Min rectangularity: 70.0% of bounding box area
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65%|██████▍ | 441/679 [00:00<00:00, 1480.97it/s]
87%|████████▋ | 590/679 [00:00<00:00, 1483.63it/s]
100%|██████████| 679/679 [00:00<00:00, 1482.94it/s]
Regularization completed: - Total objects: 679 - Rectangular objects: 648 (95.4%) - Other regularized objects: 0 (0.0%) - Unmodified objects: 31 (4.6%)
Visualize building footprints¶
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gdf.head()
gdf.head()
Out[11]:
| geometry | confidence | class | |
|---|---|---|---|
| 624 | POLYGON ((455181.6 5277618.6, 455177.4 5277614... | 0.994213 | 1 |
| 616 | POLYGON ((454990.2 5277628.2, 454990.2 5277627... | 0.992599 | 1 |
| 617 | POLYGON ((454855.8 5277628.2, 454855.8 5277626... | 0.989381 | 1 |
| 278 | POLYGON ((454981.8 5277798, 454981.2 5277797.4... | 0.986561 | 1 |
| 279 | POLYGON ((455052 5277904.8, 455050.8 5277903.6... | 0.982763 | 1 |
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geoai.view_vector_interactive(
gdf, column="confidence", layer_name="Building", tiles="Satellite"
)
geoai.view_vector_interactive(
gdf, column="confidence", layer_name="Building", tiles="Satellite"
)
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Make this Notebook Trusted to load map: File -> Trust Notebook
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geoai.view_vector_interactive(
gdf, column="confidence", layer_name="Building", tiles=raster_url
)
geoai.view_vector_interactive(
gdf, column="confidence", layer_name="Building", tiles=raster_url
)
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Make this Notebook Trusted to load map: File -> Trust Notebook
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geoai.view_vector_interactive(
gdf_regularized, column="confidence", layer_name="Building", tiles=raster_url
)
geoai.view_vector_interactive(
gdf_regularized, column="confidence", layer_name="Building", tiles=raster_url
)
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Make this Notebook Trusted to load map: File -> Trust Notebook
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extractor.visualize_results(raster_path, gdf, output_path="naip_buildings.png")
extractor.visualize_results(raster_path, gdf, output_path="naip_buildings.png")
Using confidence values (range: 0.50 - 0.99)
Visualization saved to naip_buildings.png
Sample visualization saved to naip_buildings_sample.png
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extractor.visualize_results(
raster_path, gdf_regularized, output_path="naip_buildings_regularized.png"
)
extractor.visualize_results(
raster_path, gdf_regularized, output_path="naip_buildings_regularized.png"
)
Using confidence values (range: 0.50 - 0.99)
Visualization saved to naip_buildings_regularized.png
Sample visualization saved to naip_buildings_regularized_sample.png